# Large Foundation Models for Power Systems

arXiv: https://arxiv.org/abs/2312.07044 · HTML: https://mlsanity.com/paper/2312.07044/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 33 references that are in the map.
- First posted on arXiv 12 Dec 2023.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs](https://mlsanity.com/paper/2307.16789/) (2023)
- [Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?](https://mlsanity.com/paper/2202.12837/) (2022)
- [Large Language Models as Optimizers](https://mlsanity.com/paper/2309.03409/) (2023)
- [The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision)](https://mlsanity.com/paper/2309.17421/) (2023)
- [Multimodal Few-Shot Learning with Frozen Language Models](https://mlsanity.com/paper/2106.13884/) (2021)
- [A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT](https://mlsanity.com/paper/2302.11382/) (2023)
- [AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts](https://mlsanity.com/paper/2110.01691/) (2021)
- [Towards Generic Anomaly Detection and Understanding: Large-scale Visual-linguistic Model (GPT-4V) Takes the Lead](https://mlsanity.com/paper/2311.02782/) (2023)
- [Machine Learning for AC Optimal Power Flow](https://mlsanity.com/paper/1910.08842/) (2019)
- [A Human-on-the-Loop Optimization Autoformalism Approach for Sustainability](https://mlsanity.com/paper/2308.10380/) (2023)
- [State-of-Charge Aware EV Charging](https://mlsanity.com/paper/2111.15147/) (2021)

## Built on by (most cited first, 18 of 18)

- [Towards Urban General Intelligence: A Review and Outlook of Urban Foundation Models](https://mlsanity.com/paper/2402.01749/) (2024)
- [Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework](https://mlsanity.com/paper/2411.16707/) (2024)
- [RL2: Reinforce Large Language Model to Assist Safe Reinforcement Learning for Energy Management of Active Distribution Networks](https://mlsanity.com/paper/2412.01303/) (2024)
- [Enabling Large Language Models to Perform Power System Simulations with Previously Unseen Tools: A Case of Daline](https://mlsanity.com/paper/2406.17215/) (2024)
- [Large Language Model Powered Automated Modeling and Optimization of Active Distribution Network Dispatch Problems](https://mlsanity.com/paper/2507.21162/) (2025)
- [PowerGraph-LLM: Novel Power Grid Graph Embedding and Optimization with Large Language Models](https://mlsanity.com/paper/2501.07639/) (2025)
- [Large Language Model Interface for Home Energy Management Systems](https://mlsanity.com/paper/2501.07919/) (2025)
- [From Natural Language to Solver-Ready Power System Optimization: An LLM-Assisted, Validation-in-the-Loop Framework](https://mlsanity.com/paper/2508.08147/) (2025)
- [Large Language Model-Empowered Interactive Load Forecasting](https://mlsanity.com/paper/2505.16577/) (2025)
- [LLM-Enhanced Multi-Agent Reinforcement Learning with Expert Workflow for Real-Time P2P Energy Trading](https://mlsanity.com/paper/2507.14995/) (2025)
- [LLM4DistReconfig: A Fine-tuned Large Language Model for Power Distribution Network Reconfiguration](https://mlsanity.com/paper/2501.14960/) (2025)
- [Large Language Model-Assisted Planning of Electric Vehicle Charging Infrastructure with Real-World Case Study](https://mlsanity.com/paper/2511.19055/) (2025)
- [Knowledge boundary probing and demand-guided intervention for LLM-based power system code generation](https://mlsanity.com/paper/2605.31478/) (2026)
- [Automated Heuristic Design for Unit Commitment Using Large Language Models](https://mlsanity.com/paper/2506.12495/) (2025)
- [Keep the Lights On, Keep the Lengths in Check: Plug-In Adversarial Detection for Time-Series LLMs in Energy Forecasting](https://mlsanity.com/paper/2512.12154/) (2025)
- [LLMs and Agentic AI Systems for Smart Grids: A Tutorial on Architectures and Applications](https://mlsanity.com/paper/2607.18147/) (2026)
- [Training Numerical Intelligence via Auto-Diagnosis and Skill Discovery](https://mlsanity.com/paper/2610.03872/) (2026)
- [Learning by Examples: Power Grid Steady State Analysis with a Tabular Foundation Model](https://mlsanity.com/paper/2610.20462/) (2026)

## Abstract (arXiv)

Foundation models, such as Large Language Models (LLMs), can respond to a wide range of format-free queries without any task-specific data collection or model training, creating various research and application opportunities for the modeling and operation of large-scale power systems. In this paper, we outline how such large foundation model such as GPT-4 are developed, and discuss how they can be leveraged in challenging power and energy system tasks. We first investigate the potential of existing foundation models by validating their performance on four representative tasks across power system domains, including the optimal power flow (OPF), electric vehicle (EV) scheduling, knowledge retrieval for power engineering technical reports, and situation awareness. Our results indicate strong capabilities of such foundation models on boosting the efficiency and reliability of power system operational pipelines. We also provide suggestions and projections on future deployment of foundation models in power system applications.